Applied Forward Deployed Engineer, Monte Carlo
$140-180k
Offers equity and commission
Data observability platform
Open for applications
Data observability platform
501-1000 employees
Open for applications
$140-180k
Offers equity and commission
501-1000 employees
Company mission
To accelerate the world’s adoption of data by reducing data downtime.
Role
Who you are
- 1–3 years in a technical role where you owned deployment or integration work in customer or enterprise environments — implementation engineering, integration engineering, applied engineering, or similar. You've built things that actually ran in production at someone else's company
- Hands-on with Python and SQL. Comfortable working with REST APIs in real environments, not just in tutorials. You know how to debug integrations when they break in the field
- Current on where AI tooling is heading and has applied it in actual work. You're not just familiar with the concepts — you've shipped something with it
- Strong written and verbal communication. You'll document customer needs, brief Engineering regularly, and be the person customers trust to translate technical complexity into something actionable
What the job involves
- This is a field engineering role for someone who can sit across from a customer, understand what they're trying to build, and go write it
- You'll prototype integrations and connectors based on real customer requests, conduct rapid field testing in live environments, and relay what you learn back to Engineering so the best solutions make it into the core product
- You're the link between what customers need today and what Monte Carlo ships tomorrow
- Meet with customers to understand their technical requirements, data environments, and integration gaps — then translate those conversations into working prototypes
- Build integrations and connectors tailored to customer data stacks (Snowflake, Databricks, Salesforce, and others) using Python, SQL, and REST APIs
- Run rapid field testing in customer environments — ship fast, validate fast, iterate fast
- Document what you learn and relay it back to Engineering: what customers are asking for, what worked, what didn't, and why
- Work alongside senior FDEs to hand off proven solutions that can scale into the core product
- Become a trusted technical contact for your customers — someone they loop in early, not after something breaks
- The FDE team at Monte Carlo is a small, rapid-prototyping unit in the field. The team handles customer-specific technical integration requests without constraining the core product roadmap — FDEs prototype solutions, conduct rapid field testing, and relay what they learn back to Engineering and Product
Our take
Dozens of billion-dollar companies have emerged with a shared focus on easing the burden on engineering teams constantly battling to guarantee reliability in their products. For example, New Relic revolutionised application observability. In the same way, Monte Carlo is tackling data observability.
The service pulls information from metadata log metrics to help teams understand their data health, and if something breaks, the product will send proactive notifications. The product is an off-the-shelf SaaS offering that customers can integrate and customise over time.
The company has won some major clients including big tech companies like Affirm and Intuit, but also JetBlue, PepsiCo, CNN, The New York Times, Asics, Rivian and others from many sectors. Its goal is to help companies reduce data downtime and the time data engineers must spend on tedious data quality tasks.
'Data Observability’ became a point of furore in venture capital in 2022. For example, in the span of one week, just three companies raised more than $400 million between them. Staying at the forefront of this industry is an interesting challenge for Monte Carlo, and it met it in 2024 with product enhancements that include new GenAI capabilities for its platform.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
32% employee growth in 12 months
Company
Funding (last 2 of 4 rounds)
May 2022
$135m
SERIES D
Aug 2021
$60m
SERIES C
Total funding: $236m
Company HQ
Dolores Heights, San Francisco, CA
Leadership
Barr Moses
(Co-Founder & CEO)Previously VP of Customer Success Operations for Gainsight. Originally trained as a Management Consultant with Bain & Company. Also attended Stanford where they studied Mathematics and Computer Science.
Lior Gavish
(Co-Founder & CTO)Previously SVP of Engineering for another tech startup, Barracuda. Also previously worked for PayPal and holds an MBA from Stanford University.
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